\section{Conclusion and Future Work}

The data set we had was extremely skewed towards negative samples especially in case of the appetency data. Thus Naive bayesian performs the best in terms of Balanced Accuracy for Appetency. Decision Tree on the other hand, handled upselling well and performed close to naive bayesian for churn. SVM results for churn also point towards good handling of less skewed data, although we need to delve further in order to understand its non performance in upselling.

\noindent In addition for Decision tree we would like to explore the SMOTE\cite{smote} technique for oversampling the dataset in order to improve results of appetency(highly skewed data).

\noindent For SVM, we believe that better handling of categorical data can give us improved performance, especially as our gainratio ranking had a high number of categorical variables amongst the top ranking variables.

\noindent Moreover, the heterogeneity of classifier performance suggests that an ensemble approach can give a good result for all applications. This would form the bulk of our future work. Additionally we would also like to explore boosting and bagging approaches for using classifier heterogeneity.



